Lateral Intelligence Platform (LIP)

A state-of-the-art machine learning API developed in-house by some of Europe’s top machine learning experts.

Best of both worlds

LIP provides the ultimate recommender. A hybrid that is able to process both text content and user behaviour to automatically make recommendations of relevant information.

More info

More info

More info

Text-based Recommender

lateral Hybrid-Recommender

By combining content-matching and collaborative filtering, LIP serves recommendations of the highest quality. It can both recommend content that is thematically relevant to a specific article (based on the text) and also from different interest areas (based on users with shared interests). In addition, it does not suffer from the cold start problem, i.e. the inability to make recommendations when there is a lack of user data, especially for new articles and new users.

A key benefit of recommendations, over traditional search is that users see content they are interested in or need and often also content they did not even know existed, without having to actively try to find it.

LIP Hybrid-Recommender

Text-based Recommender

The core of LIP’s technology is a unique way of measuring the thematic similarity of documents. Using machine learning, our algorithm is able to automatically process millions of documents and gain an understanding of how words relate to each other. It can then apply this to document databases it has not seen before and make intelligent recommendations of similar content.

Collaborative Filtering

This technique looks at a user’s behaviour, e.g. what they viewed, saved and purchased, in order to determine their taste. This is then compared to the behaviour of other users, in order to make relevant, personalised content recommendations. Hence, in a very basic scenario, if user A likes 3 items and user B likes 2 of those items, the likelihood of user B liking the third item user A likes is high.

Text-based recommender

The core of LIP’s technology is a unique way of measuring the thematic similarity of documents. Using machine learning, our algorithm is able to automatically process millions of documents and gain an understanding of how words relate to each other. It can then apply this to document databases it has not seen before and make intelligent recommendations of similar content.

Collaborative Filtering

This technique looks at a user’s behaviour, e.g. what they viewed, saved and purchased, in order to determine their taste. This is then compared to the behaviour of other users, in order to make relevant, personalised content recommendations. Hence, in a very basic scenario, if user A likes 3 items and user B likes 2 of those items, the likelihood of user B liking the third item user A likes is high.

LIP Hybrid-Recommender

By combining content-matching and collaborative filtering, LIP serves recommendations of the highest quality. It can both recommend content that is thematically relevant to a specific article (based on the text) and also from different interest areas (based on users with shared interests). In addition, it does not suffer from the cold start problem, i.e. the inability to make recommendations when there is a lack of user data, especially for new articles and new users.

A key benefit of recommendations, over traditional search is that users see content they are interested in or need and often also content they did not even know existed, without having to actively try to find it.

ML Features

On top of the hybird recommender through our API we offer innovate machine learning (ML) features. These include time savers like automated keyword extraction and tagging, as well as clustering which can provide you with a great overview of your data.